Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
code
Size:
10K - 100K
License:
Fix NonMatchingSplitsSizesError
#3
by
albertvillanova
HF staff
- opened
- README.md +6 -6
- dataset_infos.json +1 -1
README.md
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@@ -28,16 +28,16 @@ dataset_info:
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dtype: string
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splits:
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- name: train
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num_bytes:
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num_examples:
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- name: validation
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num_bytes:
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num_examples:
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- name: test
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num_bytes:
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num_examples: 12000
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download_size: 8658581
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dataset_size:
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---
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# Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
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dtype: string
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splits:
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- name: train
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+
num_bytes: 20179075
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num_examples: 32500
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- name: validation
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num_bytes: 6382433
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num_examples: 8500
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- name: test
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num_bytes: 7227506
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num_examples: 12000
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download_size: 8658581
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dataset_size: 33789014
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---
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# Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
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dataset_infos.json
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{"default": {"description": "CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104\n\nGiven a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.\nWe use POJ-104 dataset on this task.", "citation": "@inproceedings{mou2016convolutional,\ntitle={Convolutional neural networks over tree structures for programming language processing},\nauthor={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},\nbooktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},\npages={1287--1293},\nyear={2016}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "code": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "label", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_clone_detection_poj104", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes":
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{"default": {"description": "CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104\n\nGiven a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.\nWe use POJ-104 dataset on this task.", "citation": "@inproceedings{mou2016convolutional,\ntitle={Convolutional neural networks over tree structures for programming language processing},\nauthor={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},\nbooktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},\npages={1287--1293},\nyear={2016}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "code": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "label", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_clone_detection_poj104", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 20179075, "num_examples": 32500, "dataset_name": "code_x_glue_cc_clone_detection_poj104"}, "validation": {"name": "validation", "num_bytes": 6382433, "num_examples": 8500, "dataset_name": "code_x_glue_cc_clone_detection_poj104"}, "test": {"name": "test", "num_bytes": 7227506, "num_examples": 12000, "dataset_name": "code_x_glue_cc_clone_detection_poj104"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-POJ-104/dataset/programs.tar.gz": {"num_bytes": 8658581, "checksum": "c0b8ef3ee9c9159c882dc9337cb46da0e612a28e24852a83f8a1cd68c838f390"}}, "download_size": 8658581, "post_processing_size": null, "dataset_size": 33789014, "size_in_bytes": 42447595}}
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